A copula-based GLMM model for multivariate longitudinal data with mixed-types of responses
A new nonparametric model is presented for the analysis of multivariate longitudinal data with mixed types, including continuous, count and binary responses. The historical context of the problem is presented. All components of a \textit{copula based generalized linear mixed model} are described in detail. A pair-copula construction is adopted to measure the dependency structure between different responses. The \textit{expectation-maximization} algorithm is used to obtain the maximum likelihood estimates of the model parameters. The proposed model is applied to a real data. The simulation results are also presented. According to the authors both simulations and real data analysis show that the nonparametric models are more efficient and flexible than the parametric models.
scientific article; zbMATH DE number 7342605
scientific article